albertsr / anomaly-detection Goto Github PK

def determine_trainset(self):
    weighted_score = self.cal_weighted_score()
    min_score, max_score, median_score = [func(weighted_score) for func in (np.min, np.max, np.median)]
    anomalies_score = weighted_score[:len(self.anomalies)]
    unlabel_scores = weighted_score[len(self.anomalies):]
    
    self.alpha = np.mean(anomalies_score) if self.alpha == 'auto' else self.alpha
    #change total_score ?? but i canot find total_score
    self.beta = median_score if median_score < self.alpha else np.percentile(total_score, 45)
    assert self.beta < self.alpha, 'beta should be smaller than alpha.'